From in Fig.1; where: V_PV is the PV output

Fromthe above literature review, the authors are motivated to work on AI basedtechniques available in order to get MPP effectively in an efficient manner. Furtherthey are also encouraged to work on dSPACE and PV emulator. Consideringthe above facts, in this paper, various AI based MPPT techniques are designedsuch as ANN, FLC and CSA to track maximum power from PV system. A comparativeanalysis of different advanced MPPT techniques has been described in thispaper. The performance of the methods is evaluated by MATLAB/Simulink.  The proposed system is then implementedexperimentally using PV emulator and dSPACEDS1202 MicroLabBox.

By taking motivation from literature review, ananalysis of three MPPT techniques i.e. ANN, FLC and CSA are compared withconventional Perturb and Observe (P&O). The efficiency and accuracy of allthe techniques are calculated to arrive at the conclusion for better technique.Thepaper is organized as follows: In section 2 system configuration is described.Section 3 shows the various proposed MPPT algorithms to track maximum powerpoint and section 4 deals with results and discussion.

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In section 5 the detailsabout results are discussed. In last section, references are placed.1.  SystemConfiguration This sectiondescribes details about the Simulink and hardware modelling of PV system. PVsystem connected to load is shown in Fig.1; where: V_PV is the PV outputvoltage and I_PV is the PV output current. The output voltage of PV cell isintroduced to the DC-DC boost converter to increase voltage to an appropriaterequired level.

The boost-converter is further connected to Resistive load of60 W. PV cells have non-linear  P-V(Power-Voltage) and I-V (Current-Voltage) characteristics as given in Fig. 2.The duty cycle of boost converter is maintained to get MPP 16.

This sectionshows the proposed model for the implementation of various MPPT techniques.Sub-points in this section express details about the PV module, MPPT, DC-DCconverter and the connected load.